1 GWAS Strategy

GWAS was run using MLM model in GCTA1.93.2. Note that I tried different strategies to directly fit covariates or pre-adjust phenotypes by the covariates. The beta correlation between different strategies will be shown as below.

Take SRS_RMB_sum in Probands (with FSIQ included as covariate) for example, I tried:

  • Strategy 1: Phenotype pre-adjusted by age, sex, chip, FSIQ
  • Strategy 2: Phenotype pre-adjusted by age, sex, chip, FSIQ and 20 PCs
  • Strategy 3: directly fit age, sex, chip, FSIQ
  • Strategy 4: directly fit age, sex, chip, FSIQ and 20 PCs
#grid.raster(readPNG("figures/beta_strategy.png")
grid.raster(readPNG("figures/beta_strategy.png"))
Beta Correlation between Different GWAS Strategies

Beta Correlation between Different GWAS Strategies


Note that considering the GWAS sample size, computational time and false positive rates, we will report the results below:

  • based on Strategy2 for the same phenotype
  • based on sum measurement for the same phenotype


2 Probands

2.1 All Individuals

  • GWAS was run on all individuals including diverse ancestry backgrounds.
  • Signals with association p-value < 1e-5 will be shown.

2.1.1 Association Summary

2.1.1.1 Fitting FSIQ

datatable(iqs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


2.1.1.2 Not fitting FSIQ

datatable(noiqs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


2.1.2 Manhattan Plot

2.1.2.1 Primary Variable

2.1.2.1.1 Fitting FSIQ
#grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_primary_withPCs.png")
img <- readPNG("figures/manhplot_probands_adjIQ_1e-5_primary_withPCs.png")
grid.raster(img)


2.1.2.1.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_primary_withPCs.png"))


2.1.2.2 Secondary Variable

2.1.2.2.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_secondary_withPCs.png"))


2.1.2.2.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_secondary_withPCs.png"))


2.2 Europeans Only

  • 6861972 QCd SNPs with MAF > 0.01 included
  • 1946 European individuals are included
  • Signals with association p-value < 1e-5 will be shown.


2.2.1 Association Summary

2.2.1.1 Fitting FSIQ

datatable(iqs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


2.2.1.2 Not fitting FSIQ

datatable(noiqs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


2.2.2 Manhattan Plot

2.2.2.1 Primary Variable

2.2.2.1.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_primary_EUR_withPCs.png"))


2.2.2.1.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_primary_EUR_withPCs.png"))


2.2.2.2 Secondary Variable

2.2.2.2.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_adjIQ_1e-5_secondary_EUR_withPCs.png"))


2.2.2.2.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probands_noIQ_1e-5_secondary_EUR_withPCs.png"))


3 Probands & Unaffected Siblings

  • Phenotypes for Probands and Unaffected Siblings are separately pre-adjusted by covariates and then RINT.
  • Based on the phenotype distribution, we combine Probands and Unaffected Siblings.
  • We run GWAS on combined data.

3.1 All Individuals

  • GWAS was run on all individuals including diverse ancestry backgrounds.
  • Signals with association p-value < 1e-5 will be shown.

3.1.1 Association Summary

3.1.1.1 Fitting FSIQ

datatable(iqs_probSibs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


3.1.1.2 Not fitting FSIQ

datatable(noiqs_probSibs2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


3.1.2 Manhattan Plot

3.1.2.1 Primary Variable

3.1.2.1.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_primary_withPCs.png"))


3.1.2.1.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_primary_withPCs.png"))


3.1.2.2 Secondary Variable

3.1.2.2.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_secondary_withPCs.png"))


3.1.2.2.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_secondary_withPCs.png"))


3.2 Europeans Only

  • GWAS was run on European individuals with N=3544.
  • Signals with association p-value < 1e-5 will be shown.

3.2.1 Association Summary

3.2.1.1 Fitting FSIQ

datatable(iqs_probSibs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


3.2.1.2 Not fitting FSIQ

datatable(noiqs_probSibs_EUR2, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )


3.2.2 Manhattan Plot

3.2.2.1 Primary Variable

3.2.2.1.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_primary_EUR_withPCs.png"))


3.2.2.1.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_primary_EUR_withPCs.png"))


3.2.2.2 Secondary Variable

3.2.2.2.1 Fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_adjIQ_1e-5_secondary_EUR_withPCs.png"))


3.2.2.2.2 Not fitting FSIQ
grid.raster(readPNG("figures/manhplot_probandsAndUnaffSibs_noIQ_1e-5_secondary_EUR_withPCs.png"))


3.3 Heritability Estimation